Extreme Heat and Risk of Early Delivery Among Preterm and Term Pregnancies
Bibliographic record
Abstract
BACKGROUND: The relationship between ambient temperature and risk of delivery is poorly understood. We examined the association between heat and risk of delivery among preterm and term pregnancies with the use of a time-to-event design to minimize bias from seasonal variation in conception rates. METHODS: We used data on 206,929 term and 12,390 preterm singleton live births for Montreal, Canada, from June through September, 1981-2010. The exposure variables were (1) maximum daily temperatures in the week preceding birth and (2) number of consecutive days with temperatures of 32°C or above during the preceding week. We estimated hazards of delivery among preterm (<37 gestational weeks), early-term (37-38 weeks), and full-term (≥39 weeks) pregnancies for both exposures in Cox regression models, adjusting for maternal characteristics. Sensitivity analyses were carried out adjusting for markers of air pollution. RESULTS: Maximum temperatures reached at least 32°C during the preceding week for 19,829 births (9.0%). Relative to a maximum of 20°C, the hazard of delivery within term was 4% higher for maximum temperatures of 32°C or higher, but no association was found for preterm delivery. Associations were stronger with early-term than with full-term delivery. Extreme heat episodes with 4 to 7 days of maximum temperature of at least 32°C were associated with a 27% greater hazard of delivery among early-term pregnancies relative to other days. CONCLUSION: High ambient temperature and extreme heat episodes may trigger earlier delivery among term births.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".